{"doc_desc":{"title":"NAIIS","idno":"DDI-NGA-FMOH-NAIIS-2018-v2.1","producers":[{"name":"University of Maryland","abbr":"UMB","affiliation":"MGIC","role":"Maintained and finalized survey documentation"},{"name":"ICF","abbr":"ICF","affiliation":"University of Maryand (UMB) ","role":"Documentation of the survey and producer of the DDI"},{"name":"National Bureau of Statistics","abbr":"NBS","affiliation":"Government of Nigeria","role":"Documentation of the study"}],"prod_date":"2021-07-25","version_statement":{"version":"Verion 2.1 (July 2021) This version is identical to version 1.0, except for the sections on Datasets and external resources that were updated."}},"study_desc":{"title_statement":{"idno":"NGA-FMOH-NAIIS-2018-v2.1","title":"Nigeria HIV-AIDS Indicator and Impact Survey (NAIIS) 2018","alternate_title":"NAIIS 2018","translated_title":"No Translation"},"authoring_entity":[{"name":"Federal Ministry of Health (FMOH)","affiliation":"Government of Nigeria"},{"name":"National Agency for the Control of AIDS (NACA)","affiliation":"Government of Nigeria"},{"name":"University of Maryland (UMB)","affiliation":""},{"name":"","affiliation":""}],"oth_id":[{"name":"NAIIS National Steering & Technical Committee","affiliation":"Federal Government of Nigeria","email":"","role":"Participate in protocol development, ethical reviews, survey monitoring, data analysis, reporting writing, and dissemination of results"},{"name":"National Bureau of Statistics","affiliation":"Federal Government of Nigeria","email":"","role":"Data Collection and Analysis"}],"production_statement":{"producers":[{"name":"National Population Commission","abbr":"NPopC","affiliation":"Government of Nigeria","role":"Provided key input on sample selection"},{"name":"ICF","abbr":"ICF","affiliation":"UMB Subcontractor","role":"Designed CAPI system and primary data editing"}],"copyright":"(c) 2021","funding_agencies":[{"name":"US Centres for Disease Control and Prevention","abbr":"CDC","role":"funding"},{"name":"The Global Fund","abbr":"GF","role":"funding"}]},"distribution_statement":{"contact":[{"name":"Akipu Ehoche","affiliation":"University of Maryland (UMB)","email":"aehoche@mgic.umaryland.edu","uri":""},{"name":"Dr Adebola Bashorun","affiliation":"Federal Ministry of Health","email":"bashogee@yahoo.com","uri":""}]},"series_statement":{"series_name":"Other Household Health Survey [hh\/hea]","series_info":"First Series"},"version_statement":{"version":"Version2.1 Edited, anonymous dataset for public distribution.","version_date":"2021-07-06","version_notes":"This version of the survey document has an updated datasets and more external resources made available."},"study_info":{"keywords":[{"keyword":"HIV","vocab":"","uri":""},{"keyword":"AIDS","vocab":"","uri":""},{"keyword":"Population- based HIV Impact Assessment (PHIA)","vocab":"","uri":""}],"topics":[{"topic":"HIV","vocab":"","uri":""},{"topic":"AIDS","vocab":"","uri":""},{"topic":"Population- based HIV Impact Assessment (PHIA)","vocab":"","uri":""}],"abstract":"The 2018 Nigeria AIDS Indicator and Impact Survey (NAIIS) is a cross-sectional survey that will assess the prevalence of key human immunodeficiency virus (HIV)-related health indicators. This survey is a two-stage cluster survey of 88,775 randomly-selected households in Nigeria, sampled from among 3,551 nationally-representative sample clusters. The survey is expected to include approximately 168,029 participants, ages 15-64 years and children, ages 0-14 years, from the selected household. The 2018 NAIIS will characterize HIV incidence, prevalence, viral load suppression,  CD4 T-cell distribution, and risk behaviors in a household-based, nationally-representative sample of the population of Nigeria, and will describe uptake of key HIV prevention, care, and treatment services. The 2018 NAIIS will also estimate the prevalence of hepatitis B virus (HBV), hepatitis C virus (HCV) infections, and HBV\/HIV and HCV\/HIV co-infections.","coll_dates":[{"start":"2018","end":"2018","cycle":""}],"nation":[{"name":"Nigeria","abbreviation":"NGA"}],"geog_coverage":"National\nThe survey covered the Federal Republic and was undertaken in each state and the Federal Capital","analysis_unit":"Household Health Survey","universe":"1. Women and men aged 15-64 years living in residential households and visitors who slept in the household the night before the survey\n2. Children aged 0-14 years living in residential households and child visitors who slept in the household the night before the survey","data_kind":"Sample survey data [ssd]","notes":"Household\nAdult individual \nChild Individual"},"method":{"data_collection":{"data_collectors":[{"name":"UMB","abbr":"Univeristy of Maryland","role":"","affiliation":""}],"sampling_procedure":"This cross-sectional, household-based survey will use a two-stage cluster sampling design (enumeration area followed by households). The target population is people 15-64 and children ages 0-14 years. The overall size and distribution of the sample is determined by analysis of existing estimates of national HIV incidence, sub-national HIV prevalence, and the number of HIV-positive cases needed to obtain estimates of VLS among adults 15-64 years for each of the 36 states and the FCT while not unnecessarily inflating the sample size needed. \nFrom a sampling perspective, the three primary objectives of this proposal are based on competing demands, one focused on national incidence and the other on state-level estimates in a large number of states (37). Since the denominator used for estimating VLS is HIV-positive individuals, the required minimum number of blood draws in a stratum is inversely proportional to the expected HIV prevalence rate in that stratum. This objective requires a disproportionate amount of sample to be allocated to states with the lowest prevalence. A review of state-level prevalence estimates for sources in the last 3 to 5 years shows that state-level estimates are often divergent from one source to the next, making it difficult to ascertain the sample size needed to obtain the roughly 100 PLHIV needed to achieve a 95% confidence interval (CI) of +\/- 10 for VLS estimates. \nAn equal-size approach is proposed with a sample size of 3,700 blood specimens in each state. Three-thousand seven hundred specimens will be sufficiently large to obtain robust estimates of HIV prevalence and VLS among HIV-infected individuals in most states. In states with a HIV prevalence above 2.5%, we can anticipate 95% CI of less than +\/-10% and relative standard errors (RSEs) of less than 11% for estimates of VLS. In these states, with HIV prevalence above 2.5%, the anticipated 95% CI around prevalence is +\/- 0.7% to a high of 1.1-1.3% in states with prevalence above 6%. In states with prevalence between 1.2 and 2.5% HIV prevalence estimates would remain robust with 95% CI of +\/- 0.5-0.6% and RSE of less than 20% while 95% CI around VLS would range between 10-15% (and RSE below 15%). With this proposal only a few states, with HIV prevalence below 1.0%, would have less than robust estimates for VLS and HIV prevalence.\nThe evaluation of this \u201cequal-size\u201d approach to the 37 strata are presented in Table 1 below using the 2016 Spectrum estimates with states sorted by prevalence level from highest to lowest. This \u201cequal-size\u201d approach will ensure sufficiently large sample in each state for comparisons between states and satisfy overall need for national incidence estimate. As a result of the \u201cequal-size\u201d approach and the large number of strata (37) it is anticipated that the RSE for a national incidence estimate will be quite small, at less than 9%, when the survey is complete. It is also anticipated that regional incidence estimates (6 regions) will be possible with RSEs of 30% or less.","coll_mode":["Face-to-face [f2f]"],"research_instrument":"Household Questionnaire\nAdult Questionnaire\nEarly Adolescent Questionnaire (10-14 Years)","sources":[{"name":"","origin":"","characteristics":""}],"coll_situation":"Each field team will consist of members such as a team lead, interviewers, counselors, laboratory technicians, and driver. Survey personnel will be selected based on their qualifications and areas of expertise. The interviewers will obtain informed consent for the entire survey process. Laboratory technicians will conduct phlebotomy, as well as other duties as needed. Team members will be trained to provide home-based testing and counseling (HBTC) services which will include HIV rapid testing and counseling and point-of-care (POC) CD4 testing and return of result. The interviewers will have primary responsibility for administering the interview but the lab technicians will also receive training on how to administer the interviews. The lab technicians and interviewers will be trained in providing adult and pediatric HIV counseling. Team leads will oversee and supervise field team operations.","act_min":"Federal Ministry of Health","weight":"The following weights have been computed and are included in the data files. Use of these weights will assure that the results produced are representative. Weights are computed at the state level.\n\n1. Household weight\n\n\n2. Individual Participant weight\n\n\n3. Adolescent weight\n\n\n4. Network Scale-up Method (NSUM)\n\n\n5. Children's biometric\n\n\n6. Adult biometric","cleaning_operations":"During the household data collection, questionnaire and laboratory data were transmitted between tablets via Bluetooth connection. This facilitated synchronization of household rosters and ensured data collection for each participant followed the correct pathway. All field data collected in CSPro and the Laboratory Data Management System (LDMS) were transmitted to a central server using File Transfer Protocol Secure (FTPS) over a 4G or 3G telecommunication provider at least once a day. Questionnaire data cleaning was conducted using CSPro and SAS 9.4 (SAS Institute Inc., Cary, North Carolina, United States). Laboratory data were cleaned and merged with the final questionnaire database using unique specimen barcodes and study identification numbers.\nAll results presented in the technical report were based on weighted estimates unless otherwise stated. Analysis weights accounted for sample selection probabilities and adjusted for nonresponse and noncoverage. Nonresponse adjusted weights were calculated for households, individual interviews and individual blood draws in a hierarchical form. Adjustment for nonresponse for initial individual and bloodlevel\nweights was based on the development of weighting adjustment cells defined by a combination of variables that were potential predictors of response and HIV status. The nonresponse adjustment cells were constructed using the Chi-square Automatic Interaction Detector (CHAID) algorithm. The cells were defined based on data from the household interview for the adjustment of individual-level weights and from both the household and individual interviews for the adjustment of blood specimen-level weights.\nPost-stratification adjustments were implemented to compensate for non-coverage in the sampling process. This final adjustment calibrated the nonresponse-adjusted individual and blood weights to make the sum of each set of weights conform to national population totals by sex and five-year age groups.\nDescriptive analyses of response rates, characteristics of respondents, HIV prevalence, CD4 count distribution, HIV testing, self-reported HIV status, self-reported ART, VLS, PMTCT indicators, HBV, HCV and\nsexual behavior were conducted using SAS 9.4."},"method_notes":"Data collection was done using CSPro verion 7.2 for CAPI Android.\nIncidence estimates were based on the number of HIV infections identified as recent with the HIV-1 LAg Avidity plus VL algorithm and ARV algorithm and obtained using the formula recommended by the WHO\nIncidence Working Group and Consortium for Evaluation and Performance of Incidence Assays and with assay performance characteristics of a mean duration of recent infection (MDRI) = 130 days (95% CI: 118,\n142), a time cutoff (T) = 1.0 year and percentage false recent (PFR) = 0.00.","analysis_info":{"response_rate":"A total of 101,267 households were selected, 89,345 were occupied and 83,909 completed the household interview .\n\u2022 For adults aged 15-64 years, interview response rate was 91.6% for women and 88.2% for men; blood draw response rate was 92.9% for women and 93.6% for men.\n\u2022 For adolescents aged 10-14 years, interview response rate was 86.8% for women and 86.2% for men; blood draw response rate was 91.2% for women and 92.3% for men.\n\u2022 For children aged 0-9 years, blood draw response rate was 68.5% for women and men.","sampling_error_estimates":"Estimates from sample surveys are affected by two types of errors: non-sampling errors and sampling errors. Non-sampling errors result from mistakes made during data collection, e.g., misinterpretation of an HIV test result and data management errors such as transcription errors during data entry. While NAIIS implemented numerous quality assurance and control measures to minimize non-sampling errors, these were impossible to avoid and difficult to evaluate statistically. In contrast, sampling errors can be evaluated statistically. Sampling errors are a measure of the variability between all possible samples. The\nsample of respondents selected for NAIIS was only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples could yield\nresults that differed somewhat from the results of the actual sample selected. Although the degree of variability cannot be known exactly, it can be estimated from the survey results.\nThe standard error, which is the square root of the variance, is the usual measurement of sampling error for a statistic (e.g., proportion, mean, rate, count). In turn, the standard error can be used to calculate\nconfidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall\nwithin a range of approximately plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.\n\nNAIIS utilized a multi-stage stratified sample design, which required complex calculations to obtainsampling errors. The Taylor linearization method of variance estimation was used for survey estimates that are proportions, e.g., HIV prevalence. The Jackknife repeated replication method was used for variance estimation of more complex statistics such as rates, e.g., annual HIV incidence and counts such as the number of people living with HIV.\nThe Taylor linearization method treats any percentage or average as a ratio estimate, , where y represents the total sample value for variable y and x represents the total number of cases in the group or subgroup under consideration. The variance of r is computed using the formula given below, with the standard error being the square root of the variance: in which Where represents the stratum, which varies from 1 to H, is the total number of clusters selected in the hth stratum, is the sum of the weighted values of variable y in the ith cluster in the hth stratum, is the sum of the weighted number of cases in the ith cluster in the hth stratum and, f is the overall sampling fraction, which is so small that it is ignored.\n\nIn addition to the standard error, the design effect for each estimate is also calculated. The design effect is defined as the ratio of the standard error using the given sample design to the standard error that would result if a simple random sample had been used. A design effect of 1.0 indicates that the sample design is as efficient as a simple random sample, while a value greater than 1.0 indicates the increase in the sampling error due to the use of a more complex and less statistically efficient design. Confidence limits for the estimates, which are calculated as\n\nwhere t(0.975, K) is the 97.5th percentile of a t-distribution with K degrees of freedom, are also computed.\nSampling errors for selected variables from NAIIS are presented in Tables C.1 through C.9. For most variables, sampling error tables include the weighted estimate, unweighted denominator, standard error or\ndesign effect and lower- and upper-95% confidence limits.","data_appraisal":"Remote data quality Check was carried out using data editor"}},"data_access":{"dataset_use":{"conf_dec":[{"txt":"Confidentiality of the respondent is guranteed by law. Before accessing the data, the user will have to review the confidentiality agreement and agree to the terms and conditions provided in the access policy. \n\nBefore being granted access to the dataset, all users have to formally agree: \n1. To make no copies of any files or portions of files to which s\/he is granted access except those authorized by the data depositor. \n2. Not to use any technique in an attempt to learn the identity of any person, establishment, or sampling unit not identified on public use data files. \n3. To hold in strictest confidence the identification of any establishment or individual that may be inadvertently revealed in any documents or discussion, or analysis. Such inadvertent identification revealed in her\/his analysis will be immediately brought to the attention of the data depositor.","required":"yes","form_no":"","form_uri":""}],"contact":[{"name":"Federal Ministry of Health","affiliation":"Federal Government of Nigeria","email":"","uri":""}],"cit_req":"Federal Ministry of Health, Nigeria AIDS Indicator and Impact Survey 2018 (NAIIS), Version 1.1. Data provided through the National Archive at NBS.","conditions":"Access to the data set is determined by a review committee.\n\nPublic use files will be anonymized. FIles can be obtained under license through authority of the Ministry of Health.","disclaimer":"The Federal Ministry of Health authorizes the distribution of the data. Users of the data are required to provide a copy of their published results in order to maintain record of the citations. However, the FMoH, CDC, UMB and parties associated with the survey reprsenting the Government of Nigeria bear no responsiblity for inferences and interpretations published. Publishers can seek official validation of their results through a peer review process that has been defined by the FMoH."}}},"schematype":"survey"}